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                            <b>1.</b>
                        
                        第一章：数据分析前奏
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                            <b>1.1.</b>
                        
                        第1节：什么是数据分析
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                            <b>1.2.</b>
                        
                        第2节：环境搭建
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                            <b>1.3.</b>
                        
                        第3节：jupyternotebook使用
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                            <b>1.4.</b>
                        
                        第4节：作业
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                            <b>2.</b>
                        
                        第二章：Numpy库
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                            <b>2.2.</b>
                        
                        第2节：numpy数组基本
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                            <b>2.3.</b>
                        
                        第3节：numpy数组操作
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                            <b>2.4.</b>
                        
                        第4节：numpy索引和切片
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                            <b>2.5.</b>
                        
                        第5节：Numpy索引和切片作业
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                            <b>2.6.</b>
                        
                        第6节：numpy数组操作
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                            <b>2.7.</b>
                        
                        第7节：深拷贝和浅拷贝
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                            <b>2.8.</b>
                        
                        第8节：文件操作
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                            <b>2.9.</b>
                        
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                            <b>2.10.</b>
                        
                        第10节：NAN和INF值处理.md
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                            <b>2.11.</b>
                        
                        第11节：random模块.md
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                            <b>2.12.</b>
                        
                        第12节：axis理解.md
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                        第13节：通用函数.md
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                            <b>2.14.</b>
                        
                        第14节：numpy练习题.md
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                            <b>3.</b>
                        
                        第三章：Pandas库
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                            <b>3.1.</b>
                        
                        第1节：pandas介绍
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                            <b>3.2.</b>
                        
                        第2节：pandas索引操作
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                            <b>3.3.</b>
                        
                        第3节：pandas对齐运算
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                            <b>3.4.</b>
                        
                        第4节：pandas函数应用
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                            <b>3.5.</b>
                        
                        第5节：pandas层级索引
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                            <b>3.6.</b>
                        
                        第6节：pandas统计计算和描述
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                            <b>3.7.</b>
                        
                        第7节：文件操作
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                            <b>3.8.</b>
                        
                        第8节：数据清洗
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                            <b>3.9.</b>
                        
                        第9节：聚合和分组
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                            <b>3.10.</b>
                        
                        第10节：时间序列
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                            <b>4.</b>
                        
                        第四章：Matploblib库
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                            <b>4.1.</b>
                        
                        第1节：常用图
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                            <b>4.2.</b>
                        
                        第2节：基本使用
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                            <b>4.3.</b>
                        
                        第3节：条形图
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                            <b>4.4.</b>
                        
                        第4节：直方图
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                            <b>4.5.</b>
                        
                        第5节：散点图
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                            <b>4.6.</b>
                        
                        第6节：饼图
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                            <b>4.7.</b>
                        
                        第7节：箱线图
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                            <b>4.8.</b>
                        
                        第8节：雷达图
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                            <b>4.9.</b>
                        
                        第9节：matplotlib绘图分析
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                            <b>4.10.</b>
                        
                        第10节：多图布局
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                            <b>4.11.</b>
                        
                        第11节：matplotlib配置
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                            <b>4.12.</b>
                        
                        第12节：matplotlib作业
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                            <b>5.</b>
                        
                        第五章：Seaborn库
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                            <b>5.1.</b>
                        
                        第1节：关系绘图
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                            <b>5.2.</b>
                        
                        第2节：分类绘图
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                            <b>5.3.</b>
                        
                        第3节：分布绘图
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                            <b>5.4.</b>
                        
                        第4节：线性关系绘图
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                            <b>5.5.</b>
                        
                        第5节：FacetGrid结构图
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                            <b>5.6.</b>
                        
                        第6节：样式设置
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                            <b>5.7.</b>
                        
                        第7节：调色盘设置
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                            <b>5.8.</b>
                        
                        第8节：seaborn作业
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                            <b>6.</b>
                        
                        第六章：统计分析强化
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                            <b>6.1.</b>
                        
                        第1节：常用专业数学术语
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                            <b>6.2.</b>
                        
                        第2节：平均数
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                            <b>6.3.</b>
                        
                        第3节：方差和标准差
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                            <b>6.4.</b>
                        
                        第4节：正态分布
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                            <b>6.5.</b>
                        
                        第5节：对比分析
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                            <b>6.6.</b>
                        
                        第6节：分布分析
                    </a>
            
            
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                        第7节：交叉分析
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                        第8节：统计分析
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                            <b>6.9.</b>
                        
                        第9节：帕累托分析
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                            <b>6.10.</b>
                        
                        第10节：矩阵关联分析
                    </a>
            
            
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                            <b>6.11.</b>
                        
                        第11节：综合性分析
                    </a>
            
            
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            </ul>
            
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                            <b>7.</b>
                        
                        第七章：数据分析实战
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                            <b>7.1.</b>
                        
                        第1节：App数据分析实战
                    </a>
            
            
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                            <b>7.2.</b>
                        
                        第2节：心脏病患者数据分析
                    </a>
            
            
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                            <b>7.3.</b>
                        
                        第3节：StackoverFlow数据分析
                    </a>
            
            
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                            <b>7.4.</b>
                        
                        第4节：二手房数据分析
                    </a>
            
            
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                        <i class="fa fa-check"></i>
                        
                            <b>7.5.</b>
                        
                        第5节：吃鸡数据分析
                    </a>
            
            
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                            <b>7.6.</b>
                        
                        第6节：黑色星期五数据分析
                    </a>
            
            
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                            <b>8.</b>
                        
                        第八章：补充
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                            <b>8.1.</b>
                        
                        第1节：用Excel做数据分析
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                            <b>8.2.</b>
                        
                        第2节：echarts和pyecharts库
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                        第3节：bokeh库
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                        <h1 id="pandas&#x7684;&#x7D22;&#x5F15;&#x64CD;&#x4F5C;">Pandas&#x7684;&#x7D22;&#x5F15;&#x64CD;&#x4F5C; </h1>
<blockquote>
<h2 id="&#x7D22;&#x5F15;&#x5BF9;&#x8C61;index">&#x7D22;&#x5F15;&#x5BF9;&#x8C61;Index </h2>
</blockquote>
<h4 id="1-series&#x548C;dataframe&#x4E2D;&#x7684;&#x7D22;&#x5F15;&#x90FD;&#x662F;index&#x5BF9;&#x8C61;">1. Series&#x548C;DataFrame&#x4E2D;&#x7684;&#x7D22;&#x5F15;&#x90FD;&#x662F;Index&#x5BF9;&#x8C61; </h4>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python">print(type(ser_obj.index))
print(type(df_obj2.index))

print(df_obj2.index)
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>&lt;class &apos;pandas.indexes.range.RangeIndex&apos;&gt;
&lt;class &apos;pandas.indexes.numeric.Int64Index&apos;&gt;
Int64Index([0, 1, 2, 3], dtype=&apos;int64&apos;)
</code></pre><h4 id="2-&#x7D22;&#x5F15;&#x5BF9;&#x8C61;&#x4E0D;&#x53EF;&#x53D8;&#xFF0C;&#x4FDD;&#x8BC1;&#x4E86;&#x6570;&#x636E;&#x7684;&#x5B89;&#x5168;">2. &#x7D22;&#x5F15;&#x5BF9;&#x8C61;&#x4E0D;&#x53EF;&#x53D8;&#xFF0C;&#x4FDD;&#x8BC1;&#x4E86;&#x6570;&#x636E;&#x7684;&#x5B89;&#x5168; </h4>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x7D22;&#x5F15;&#x5BF9;&#x8C61;&#x4E0D;&#x53EF;&#x53D8;</span>
df_obj2.index[<span class="hljs-number">0</span>] = <span class="hljs-number">2</span>
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code class="lang-python">---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
&lt;ipython-input-23-7f40a356d7d1&gt; in &lt;module&gt;()
      1 # &#x7D22;&#x5F15;&#x5BF9;&#x8C61;&#x4E0D;&#x53EF;&#x53D8;
----&gt; 2 df_obj2.index[0] = 2

/Users/Power/anaconda/lib/python3.6/site-packages/pandas/indexes/base.py in __setitem__(self, key, value)
   1402 
   1403     def __setitem__(self, key, value):
-&gt; 1404         raise TypeError(&quot;Index does not support mutable operations&quot;)
   1405 
   1406     def __getitem__(self, key):

TypeError: Index does not support mutable operations
</code></pre>
<h4 id="&#x5E38;&#x89C1;&#x7684;index&#x79CD;&#x7C7B;">&#x5E38;&#x89C1;&#x7684;Index&#x79CD;&#x7C7B; </h4>
<ul>
<li>Index&#xFF0C;&#x7D22;&#x5F15;</li>
<li>Int64Index&#xFF0C;&#x6574;&#x6570;&#x7D22;&#x5F15;</li>
<li>MultiIndex&#xFF0C;&#x5C42;&#x7EA7;&#x7D22;&#x5F15;</li>
<li>DatetimeIndex&#xFF0C;&#x65F6;&#x95F4;&#x6233;&#x7C7B;&#x578B;</li>
</ul>
<blockquote>
<h2 id="series&#x7D22;&#x5F15;">Series&#x7D22;&#x5F15; </h2>
</blockquote>
<h4 id="1-index-&#x6307;&#x5B9A;&#x884C;&#x7D22;&#x5F15;&#x540D;">1. index &#x6307;&#x5B9A;&#x884C;&#x7D22;&#x5F15;&#x540D; </h4>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python">ser_obj = pd.Series(range(<span class="hljs-number">5</span>), index = [<span class="hljs-string">&apos;a&apos;</span>, <span class="hljs-string">&apos;b&apos;</span>, <span class="hljs-string">&apos;c&apos;</span>, <span class="hljs-string">&apos;d&apos;</span>, <span class="hljs-string">&apos;e&apos;</span>])
print(ser_obj.head())
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>a    0
b    1
c    2
d    3
e    4
dtype: int64
</code></pre><h4 id="2-&#x884C;&#x7D22;&#x5F15;">2. &#x884C;&#x7D22;&#x5F15; </h4>
<blockquote>
<p>ser_obj[&#x2018;label&#x2019;], ser_obj[pos]</p>
</blockquote>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x884C;&#x7D22;&#x5F15;</span>
print(ser_obj[<span class="hljs-string">&apos;b&apos;</span>])
print(ser_obj[<span class="hljs-number">2</span>])
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>1
2
</code></pre><h4 id="3-&#x5207;&#x7247;&#x7D22;&#x5F15;">3. &#x5207;&#x7247;&#x7D22;&#x5F15; </h4>
<blockquote>
<p>ser_obj[2:4], ser_obj[&#x2018;label1&#x2019;: &#x2019;label3&#x2019;]</p>
<p>&#x6CE8;&#x610F;&#xFF0C;&#x6309;&#x7D22;&#x5F15;&#x540D;&#x5207;&#x7247;&#x64CD;&#x4F5C;&#x65F6;&#xFF0C;&#x662F;&#x5305;&#x542B;&#x7EC8;&#x6B62;&#x7D22;&#x5F15;&#x7684;&#x3002;</p>
</blockquote>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x5207;&#x7247;&#x7D22;&#x5F15;</span>
print(ser_obj[<span class="hljs-number">1</span>:<span class="hljs-number">3</span>])
print(ser_obj[<span class="hljs-string">&apos;b&apos;</span>:<span class="hljs-string">&apos;d&apos;</span>])
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>b    1
c    2
dtype: int64
b    1
c    2
d    3
dtype: int64
</code></pre><h4 id="4-&#x4E0D;&#x8FDE;&#x7EED;&#x7D22;&#x5F15;">4. &#x4E0D;&#x8FDE;&#x7EED;&#x7D22;&#x5F15; </h4>
<blockquote>
<p>ser_obj[[&#x2018;label1&#x2019;, &#x2019;label2&#x2019;, &#x2018;label3&#x2019;]]</p>
</blockquote>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x4E0D;&#x8FDE;&#x7EED;&#x7D22;&#x5F15;</span>
print(ser_obj[[<span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">4</span>]])
print(ser_obj[[<span class="hljs-string">&apos;a&apos;</span>, <span class="hljs-string">&apos;e&apos;</span>]])
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>a    0
c    2
e    4
dtype: int64
a    0
e    4
dtype: int64
</code></pre><h4 id="5-&#x5E03;&#x5C14;&#x7D22;&#x5F15;">5. &#x5E03;&#x5C14;&#x7D22;&#x5F15; </h4>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x5E03;&#x5C14;&#x7D22;&#x5F15;</span>
ser_bool = ser_obj &gt; <span class="hljs-number">2</span>
print(ser_bool)
print(ser_obj[ser_bool])

print(ser_obj[ser_obj &gt; <span class="hljs-number">2</span>])
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>a    False
b    False
c    False
d     True
e     True
dtype: bool
d    3
e    4
dtype: int64
d    3
e    4
dtype: int64
</code></pre><h2 id="dataframe&#x7D22;&#x5F15;">DataFrame&#x7D22;&#x5F15; </h2>
<h4 id="1-columns-&#x6307;&#x5B9A;&#x5217;&#x7D22;&#x5F15;&#x540D;">1. columns &#x6307;&#x5B9A;&#x5217;&#x7D22;&#x5F15;&#x540D; </h4>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

df_obj = pd.DataFrame(np.random.randn(<span class="hljs-number">5</span>,<span class="hljs-number">4</span>), columns = [<span class="hljs-string">&apos;a&apos;</span>, <span class="hljs-string">&apos;b&apos;</span>, <span class="hljs-string">&apos;c&apos;</span>, <span class="hljs-string">&apos;d&apos;</span>])
print(df_obj.head())
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>          a         b         c         d
0 -0.241678  0.621589  0.843546 -0.383105
1 -0.526918 -0.485325  1.124420 -0.653144
2 -1.074163  0.939324 -0.309822 -0.209149
3 -0.716816  1.844654 -2.123637 -1.323484
4  0.368212 -0.910324  0.064703  0.486016
</code></pre><p><img src="../assets/3.png" alt=""></p>
<h4 id="2-&#x5217;&#x7D22;&#x5F15;">2. &#x5217;&#x7D22;&#x5F15; </h4>
<blockquote>
<p>df_obj[[&#x2018;label&#x2019;]]</p>
</blockquote>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x5217;&#x7D22;&#x5F15;</span>
print(df_obj[<span class="hljs-string">&apos;a&apos;</span>]) <span class="hljs-comment"># &#x8FD4;&#x56DE;Series&#x7C7B;&#x578B;</span>
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>0   -0.241678
1   -0.526918
2   -1.074163
3   -0.716816
4    0.368212
Name: a, dtype: float64
</code></pre><h4 id="3-&#x4E0D;&#x8FDE;&#x7EED;&#x7D22;&#x5F15;">3. &#x4E0D;&#x8FDE;&#x7EED;&#x7D22;&#x5F15; </h4>
<blockquote>
<p>df_obj[[&#x2018;label1&#x2019;, &#x2018;label2&#x2019;]]</p>
</blockquote>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x4E0D;&#x8FDE;&#x7EED;&#x7D22;&#x5F15;</span>
print(df_obj[[<span class="hljs-string">&apos;a&apos;</span>,<span class="hljs-string">&apos;c&apos;</span>]])
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>          a         c
0 -0.241678  0.843546
1 -0.526918  1.124420
2 -1.074163 -0.309822
3 -0.716816 -2.123637
4  0.368212  0.064703
</code></pre><blockquote>
<h2 id="&#x9AD8;&#x7EA7;&#x7D22;&#x5F15;&#xFF1A;&#x6807;&#x7B7E;&#x3001;&#x4F4D;&#x7F6E;&#x548C;&#x6DF7;&#x5408;">&#x9AD8;&#x7EA7;&#x7D22;&#x5F15;&#xFF1A;&#x6807;&#x7B7E;&#x3001;&#x4F4D;&#x7F6E;&#x548C;&#x6DF7;&#x5408; </h2>
</blockquote>
<p>Pandas&#x7684;&#x9AD8;&#x7EA7;&#x7D22;&#x5F15;&#x6709;3&#x79CD;</p>
<h4 id="1-loc-&#x6807;&#x7B7E;&#x7D22;&#x5F15;">1. loc &#x6807;&#x7B7E;&#x7D22;&#x5F15; </h4>
<blockquote>
<p>DataFrame &#x4E0D;&#x80FD;&#x76F4;&#x63A5;&#x5207;&#x7247;&#xFF0C;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;loc&#x6765;&#x505A;&#x5207;&#x7247;</p>
<p>loc&#x662F;&#x57FA;&#x4E8E;&#x6807;&#x7B7E;&#x540D;&#x7684;&#x7D22;&#x5F15;&#xFF0C;&#x4E5F;&#x5C31;&#x662F;&#x6211;&#x4EEC;&#x81EA;&#x5B9A;&#x4E49;&#x7684;&#x7D22;&#x5F15;&#x540D;</p>
</blockquote>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x6807;&#x7B7E;&#x7D22;&#x5F15; loc</span>
<span class="hljs-comment"># Series</span>
print(ser_obj[<span class="hljs-string">&apos;b&apos;</span>:<span class="hljs-string">&apos;d&apos;</span>])
print(ser_obj.loc[<span class="hljs-string">&apos;b&apos;</span>:<span class="hljs-string">&apos;d&apos;</span>])

<span class="hljs-comment"># DataFrame</span>
print(df_obj[<span class="hljs-string">&apos;a&apos;</span>])

<span class="hljs-comment"># &#x7B2C;&#x4E00;&#x4E2A;&#x53C2;&#x6570;&#x7D22;&#x5F15;&#x884C;&#xFF0C;&#x7B2C;&#x4E8C;&#x4E2A;&#x53C2;&#x6570;&#x662F;&#x5217;</span>
print(df_obj.loc[<span class="hljs-number">0</span>:<span class="hljs-number">2</span>, <span class="hljs-string">&apos;a&apos;</span>])
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>b    1
c    2
d    3
dtype: int64
b    1
c    2
d    3
dtype: int64

0   -0.241678
1   -0.526918
2   -1.074163
3   -0.716816
4    0.368212
Name: a, dtype: float64
0   -0.241678
1   -0.526918
2   -1.074163
Name: a, dtype: float64
</code></pre><h4 id="2-iloc-&#x4F4D;&#x7F6E;&#x7D22;&#x5F15;">2. iloc &#x4F4D;&#x7F6E;&#x7D22;&#x5F15; </h4>
<blockquote>
<p>&#x4F5C;&#x7528;&#x548C;loc&#x4E00;&#x6837;&#xFF0C;&#x4E0D;&#x8FC7;&#x662F;&#x57FA;&#x4E8E;&#x7D22;&#x5F15;&#x7F16;&#x53F7;&#x6765;&#x7D22;&#x5F15;</p>
</blockquote>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x6574;&#x578B;&#x4F4D;&#x7F6E;&#x7D22;&#x5F15; iloc</span>
<span class="hljs-comment"># Series</span>
print(ser_obj[<span class="hljs-number">1</span>:<span class="hljs-number">3</span>])
print(ser_obj.iloc[<span class="hljs-number">1</span>:<span class="hljs-number">3</span>])

<span class="hljs-comment"># DataFrame</span>
print(df_obj.iloc[<span class="hljs-number">0</span>:<span class="hljs-number">2</span>, <span class="hljs-number">0</span>]) <span class="hljs-comment"># &#x6CE8;&#x610F;&#x548C;df_obj.loc[0:2, &apos;a&apos;]&#x7684;&#x533A;&#x522B;</span>
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>b    1
c    2
dtype: int64
b    1
c    2
dtype: int64

0   -0.241678
1   -0.526918
Name: a, dtype: float64
</code></pre><h4 id="3-ix-&#x6807;&#x7B7E;&#x4E0E;&#x4F4D;&#x7F6E;&#x6DF7;&#x5408;&#x7D22;&#x5F15;">3. ix &#x6807;&#x7B7E;&#x4E0E;&#x4F4D;&#x7F6E;&#x6DF7;&#x5408;&#x7D22;&#x5F15; </h4>
<blockquote>
<p>ix&#x662F;&#x4EE5;&#x4E0A;&#x4E8C;&#x8005;&#x7684;&#x7EFC;&#x5408;&#xFF0C;&#x65E2;&#x53EF;&#x4EE5;&#x4F7F;&#x7528;&#x7D22;&#x5F15;&#x7F16;&#x53F7;&#xFF0C;&#x53C8;&#x53EF;&#x4EE5;&#x4F7F;&#x7528;&#x81EA;&#x5B9A;&#x4E49;&#x7D22;&#x5F15;&#xFF0C;&#x8981;&#x89C6;&#x60C5;&#x51B5;&#x4E0D;&#x540C;&#x6765;&#x4F7F;&#x7528;&#xFF0C;</p>
<p>&#x5982;&#x679C;&#x7D22;&#x5F15;&#x65E2;&#x6709;&#x6570;&#x5B57;&#x53C8;&#x6709;&#x82F1;&#x6587;&#xFF0C;&#x90A3;&#x4E48;&#x8FD9;&#x79CD;&#x65B9;&#x5F0F;&#x662F;&#x4E0D;&#x5EFA;&#x8BAE;&#x4F7F;&#x7528;&#x7684;&#xFF0C;&#x5BB9;&#x6613;&#x5BFC;&#x81F4;&#x5B9A;&#x4F4D;&#x7684;&#x6DF7;&#x4E71;&#x3002;</p>
</blockquote>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x6DF7;&#x5408;&#x7D22;&#x5F15; ix</span>
<span class="hljs-comment"># Series</span>
print(ser_obj.ix[<span class="hljs-number">1</span>:<span class="hljs-number">3</span>])
print(ser_obj.ix[<span class="hljs-string">&apos;b&apos;</span>:<span class="hljs-string">&apos;c&apos;</span>])

<span class="hljs-comment"># DataFrame</span>
print(df_obj.loc[<span class="hljs-number">0</span>:<span class="hljs-number">2</span>, <span class="hljs-string">&apos;a&apos;</span>])
print(df_obj.ix[<span class="hljs-number">0</span>:<span class="hljs-number">2</span>, <span class="hljs-number">0</span>])
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>b    1
c    2
dtype: int64
b    1
c    2
dtype: int64

0   -0.241678
1   -0.526918
2   -1.074163
Name: a, dtype: float64
</code></pre><h4 id="&#x6CE8;&#x610F;">&#x6CE8;&#x610F; </h4>
<blockquote>
<p>DataFrame&#x7D22;&#x5F15;&#x64CD;&#x4F5C;&#xFF0C;&#x53EF;&#x5C06;&#x5176;&#x770B;&#x4F5C;ndarray&#x7684;&#x7D22;&#x5F15;&#x64CD;&#x4F5C;</p>
<p>&#x6807;&#x7B7E;&#x7684;&#x5207;&#x7247;&#x7D22;&#x5F15;&#x662F;&#x5305;&#x542B;&#x672B;&#x5C3E;&#x4F4D;&#x7F6E;&#x7684;</p>
</blockquote>

                    
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